A Waveguide Vector Spectrometer (WVS), operating in the frequency range 1.6–2.7 GHz, was designed, set up and tested for rapid assessment of main chemical-physical properties of a plurality of food products. The system, completely integrated, includes a waveguide, a control unit, a signal generator, a gain/phase comparator, a power supply, and a USB port (for control and data transfer). The information contained in the gain and phase waveforms related to the product dielectric properties are exploited for the prediction of different compounds characterising both simple liquid solutions and more complex food products. By processing spectral data with Partial Least Square Regression (PLS) algorithm, high levels of prediction accuracy were observed for all the tested products (R2 values from 0.940 to 0.999, in validation). Generally, both gain and phase waveforms appeared to be effective, for the selected foods, in terms of prediction accuracy. On the whole, the proposed system seems able to assess the content of varied substances both on simple and complex matrices. Simple prospective changes of the sample holder make the equipment potentially suitable for on-line monitoring of the quality of food products.

Assessment of food compositional parameters by means of a Waveguide Vector Spectrometer

RAGNI, LUIGI;BERARDINELLI, ANNACHIARA;CEVOLI, CHIARA;IACCHERI, ELEONORA;ROMANI, ALDO
2017

Abstract

A Waveguide Vector Spectrometer (WVS), operating in the frequency range 1.6–2.7 GHz, was designed, set up and tested for rapid assessment of main chemical-physical properties of a plurality of food products. The system, completely integrated, includes a waveguide, a control unit, a signal generator, a gain/phase comparator, a power supply, and a USB port (for control and data transfer). The information contained in the gain and phase waveforms related to the product dielectric properties are exploited for the prediction of different compounds characterising both simple liquid solutions and more complex food products. By processing spectral data with Partial Least Square Regression (PLS) algorithm, high levels of prediction accuracy were observed for all the tested products (R2 values from 0.940 to 0.999, in validation). Generally, both gain and phase waveforms appeared to be effective, for the selected foods, in terms of prediction accuracy. On the whole, the proposed system seems able to assess the content of varied substances both on simple and complex matrices. Simple prospective changes of the sample holder make the equipment potentially suitable for on-line monitoring of the quality of food products.
Ragni, Luigi; Berardinelli, Annachiara; Cevoli, Chiara; Filippi, Matteo; Iaccheri, Eleonora; Romani, Aldo
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/586210
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